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  <div class="section" id="numpy-average">
<h1>numpy.average<a class="headerlink" href="#numpy-average" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="numpy.average">
<code class="sig-prename descclassname">numpy.</code><code class="sig-name descname">average</code><span class="sig-paren">(</span><em class="sig-param">a</em>, <em class="sig-param">axis=None</em>, <em class="sig-param">weights=None</em>, <em class="sig-param">returned=False</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/numpy/numpy/blob/v1.18.1/numpy/lib/function_base.py#L293-L432"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#numpy.average" title="Permalink to this definition">¶</a></dt>
<dd><p>Compute the weighted average along the specified axis.</p>
<dl class="field-list">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl>
<dt><strong>a</strong><span class="classifier">array_like</span></dt><dd><p>Array containing data to be averaged. If <em class="xref py py-obj">a</em> is not an array, a
conversion is attempted.</p>
</dd>
<dt><strong>axis</strong><span class="classifier">None or int or tuple of ints, optional</span></dt><dd><p>Axis or axes along which to average <em class="xref py py-obj">a</em>.  The default,
axis=None, will average over all of the elements of the input array.
If axis is negative it counts from the last to the first axis.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 1.7.0.</span></p>
</div>
<p>If axis is a tuple of ints, averaging is performed on all of the axes
specified in the tuple instead of a single axis or all the axes as
before.</p>
</dd>
<dt><strong>weights</strong><span class="classifier">array_like, optional</span></dt><dd><p>An array of weights associated with the values in <em class="xref py py-obj">a</em>. Each value in
<em class="xref py py-obj">a</em> contributes to the average according to its associated weight.
The weights array can either be 1-D (in which case its length must be
the size of <em class="xref py py-obj">a</em> along the given axis) or of the same shape as <em class="xref py py-obj">a</em>.
If <em class="xref py py-obj">weights=None</em>, then all data in <em class="xref py py-obj">a</em> are assumed to have a
weight equal to one.  The 1-D calculation is:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">avg</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">a</span> <span class="o">*</span> <span class="n">weights</span><span class="p">)</span> <span class="o">/</span> <span class="nb">sum</span><span class="p">(</span><span class="n">weights</span><span class="p">)</span>
</pre></div>
</div>
<p>The only constraint on <em class="xref py py-obj">weights</em> is that <em class="xref py py-obj">sum(weights)</em> must not be 0.</p>
</dd>
<dt><strong>returned</strong><span class="classifier">bool, optional</span></dt><dd><p>Default is <em class="xref py py-obj">False</em>. If <em class="xref py py-obj">True</em>, the tuple (<a class="reference internal" href="#numpy.average" title="numpy.average"><code class="xref py py-obj docutils literal notranslate"><span class="pre">average</span></code></a>, <em class="xref py py-obj">sum_of_weights</em>)
is returned, otherwise only the average is returned.
If <em class="xref py py-obj">weights=None</em>, <em class="xref py py-obj">sum_of_weights</em> is equivalent to the number of
elements over which the average is taken.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>retval, [sum_of_weights]</strong><span class="classifier">array_type or double</span></dt><dd><p>Return the average along the specified axis. When <em class="xref py py-obj">returned</em> is <em class="xref py py-obj">True</em>,
return a tuple with the average as the first element and the sum
of the weights as the second element. <em class="xref py py-obj">sum_of_weights</em> is of the
same type as <em class="xref py py-obj">retval</em>. The result dtype follows a genereal pattern.
If <em class="xref py py-obj">weights</em> is None, the result dtype will be that of <em class="xref py py-obj">a</em> , or <code class="docutils literal notranslate"><span class="pre">float64</span></code>
if <em class="xref py py-obj">a</em> is integral. Otherwise, if <em class="xref py py-obj">weights</em> is not None and <em class="xref py py-obj">a</em> is non-
integral, the result type will be the type of lowest precision capable of
representing values of both <em class="xref py py-obj">a</em> and <em class="xref py py-obj">weights</em>. If <em class="xref py py-obj">a</em> happens to be
integral, the previous rules still applies but the result dtype will
at least be <code class="docutils literal notranslate"><span class="pre">float64</span></code>.</p>
</dd>
</dl>
</dd>
<dt class="field-odd">Raises</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>ZeroDivisionError</strong></dt><dd><p>When all weights along axis are zero. See <a class="reference internal" href="numpy.ma.average.html#numpy.ma.average" title="numpy.ma.average"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.ma.average</span></code></a> for a
version robust to this type of error.</p>
</dd>
<dt><strong>TypeError</strong></dt><dd><p>When the length of 1D <em class="xref py py-obj">weights</em> is not the same as the shape of <em class="xref py py-obj">a</em>
along axis.</p>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<p><a class="reference internal" href="numpy.mean.html#numpy.mean" title="numpy.mean"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mean</span></code></a></p>
<dl class="simple">
<dt><a class="reference internal" href="numpy.ma.average.html#numpy.ma.average" title="numpy.ma.average"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ma.average</span></code></a></dt><dd><p>average for masked arrays – useful if your data contains “missing” values</p>
</dd>
<dt><a class="reference internal" href="numpy.result_type.html#numpy.result_type" title="numpy.result_type"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.result_type</span></code></a></dt><dd><p>Returns the type that results from applying the numpy type promotion rules to the arguments.</p>
</dd>
</dl>
</div>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">data</span>
<span class="go">array([1, 2, 3, 4])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">average</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
<span class="go">2.5</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">average</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">11</span><span class="p">),</span> <span class="n">weights</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">))</span>
<span class="go">4.0</span>
</pre></div>
</div>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">6</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">data</span>
<span class="go">array([[0, 1],</span>
<span class="go">       [2, 3],</span>
<span class="go">       [4, 5]])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">average</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">weights</span><span class="o">=</span><span class="p">[</span><span class="mf">1.</span><span class="o">/</span><span class="mi">4</span><span class="p">,</span> <span class="mf">3.</span><span class="o">/</span><span class="mi">4</span><span class="p">])</span>
<span class="go">array([0.75, 2.75, 4.75])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">average</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">weights</span><span class="o">=</span><span class="p">[</span><span class="mf">1.</span><span class="o">/</span><span class="mi">4</span><span class="p">,</span> <span class="mf">3.</span><span class="o">/</span><span class="mi">4</span><span class="p">])</span>
<span class="gt">Traceback (most recent call last):</span>
    <span class="o">...</span>
<span class="gr">TypeError</span>: <span class="n">Axis must be specified when shapes of a and weights differ.</span>
</pre></div>
</div>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float128</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">complex64</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">avg</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">average</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">weights</span><span class="o">=</span><span class="n">w</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">avg</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
<span class="go">complex256</span>
</pre></div>
</div>
</dd></dl>

</div>


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